Method and system for biometric authentication

ABSTRACT

A method of authentication is provided that includes capturing biometric data for a desired biometric type from an individual, determining an algorithm for converting the biometric data into authentication words, converting the captured biometric data into authentication words in accordance with the determined algorithm, including the authentication words in a probe, and comparing the probe against identity records stored in a server system. Each of the identity records includes enrollment biometric words of an individual obtained during enrollment. Moreover, the method includes identifying at least one of the identity records as a potential matching identity record when at least one of the authentication words included in the probe matches at least one of the enrollment biometric words included in the at least one identity record, and generating a list of potential matching identity records.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation application of U.S. patent application Ser. No.12/857,337, filed Aug. 16, 2010, now U.S. Pat. No. 8,041,956, issuedOct. 18, 2011, the disclosure of which is incorporated herein byreference.

BACKGROUND OF THE INVENTION

This invention relates generally to authenticating individuals, and moreparticularly, to a method and system for biometric authentication.

Generally, biometric authentication systems are used to identify andverify the identity of individuals and are used in many differentcontexts such as verifying the identity of individuals entering acountry using electronic passports. Biometric authentication systemshave also been known to verify the identity of individuals usingdriver's licenses, traveler's tokens, employee identity cards andbanking cards.

Known biometric authentication system search engines generally identifyindividuals using biometric feature templates derived from raw biometricdata captured from individuals. Specifically, a biometric featuretemplate derived from biometric data captured from an individual duringauthentication is compared against a database of previously derivedbiometric feature templates, and the identity of the individual isverified upon determining a match between one of the stored biometricfeature templates and the biometric feature template derived duringauthentication. However, comparing biometric feature templates against adatabase of biometric feature templates may place substantial demands oncomputer system memory and processing which may result in unacceptablylong authentication periods. Moreover, such known biometricauthentication system search engines are generally highly specializedand proprietary.

By virtue of being highly specialized and proprietary it has been knownto be difficult, time consuming and costly to modify known biometricauthentication search engines to operate with other authenticationsystems. Furthermore, known biometric authentication search engines, byvirtue of evaluating only biometric data of an individual forauthentication, in many cases, do not provide an adequate amount ofinformation about the individual to yield consistently accurateauthentication results.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect of the invention, a method of authentication is provided.The method includes capturing biometric data for a desired biometrictype from an individual, determining an algorithm for converting thebiometric data into authentication words, converting the capturedbiometric data into authentication words in accordance with thedetermined algorithm, including the authentication words in a probe, andcomparing the probe against identity records stored in a server system.Each of the identity records includes enrollment biometric words of anindividual obtained during enrollment. Moreover, the method includesidentifying at least one of the identity records as a potential matchingidentity record when at least one of the authentication words includedin the probe matches at least one of the enrollment biometric wordsincluded in the at least one identity record, and generating a list ofpotential matching identity records.

In another aspect of the invention, a system for biometricauthentication is provided. The system includes a computer configured asa server. The server includes at least a data base and is configured tostore within the database at least one conversion algorithm and at leasta gallery of data including identity records. Each identity recordincludes at least biographic data of an individual and enrollmentbiometric words of the individual. The at least one client systemincludes at least a computer configured to communicate with the server.The client system is configured to at least capture biometric data forat least one desired biometric type from an individual.

The server is also configured to convert the captured biometric datainto authentication words by executing the at least one conversionalgorithm. The at least one conversion algorithm is configured togenerate the enrollment biometric words. Moreover, the server isconfigured to generate a probe including at least the authenticationwords, compare the probe against the gallery, and identify at least oneof the identity records as a matching identity record when at least oneof the authentication words matches at least one of the enrollmentbiometric words included in the at least one identity record.Furthermore, the server is configured to generate a list of potentialmatching identity records.

In yet another aspect of the invention, a method of text-based biometricauthentication is provided. The method includes capturing biometric datafor a plurality of different biometric types from an individual anddetermining a plurality of algorithms. Each of the algorithms isoperable to convert captured biometric data of a corresponding biometrictype into a vocabulary of words. Moreover, the method includesconverting the captured biometric data for each biometric type intoauthentication words in accordance with the corresponding one of thealgorithms and comparing a probe against identity records stored in aserver system. The probe includes authentication words and biographicwords, and each of the identity records includes at least enrollmentbiometric words and biographic words of a corresponding individualobtained during enrollment. Furthermore, the method includes identifyingat least one of the identity records as a potential matching identityrecord when at least one of the biographic words included in the probeor at least one of the authentication words included in the probematches at least one of the biographic words or one of the enrollmentbiometric words, respectively, included in the at least one identityrecord. The method also includes generating a list of potential matchingidentity records.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary embodiment of a serverarchitecture of a computer system used for authenticating the identityof an individual;

FIG. 2 is a plan view of an exemplary fingerprint image of processedbiometric data;

FIG. 3 is the plan view of the exemplary fingerprint image as shown inFIG. 2 including concentric circles positioned thereon;

FIG. 4 is the plan view of the exemplary fingerprint image as shown inFIG. 2 further including a radial grid positioned thereon fordetermining exemplary words from biometric data;

FIG. 5 is an enlarged partial plan view of FIG. 4, further includingoverlapping border regions;

FIG. 6 is the plan view of the exemplary fingerprint image and radialgrid as shown in FIG. 4 and is for determining alternative exemplarywords from biometric data;

FIG. 7 is an exemplary identity record including biographic data, typesof biometric data and words;

FIG. 8 is an alternative exemplary identity record including biographicdata, types of biometric data and words;

FIG. 9 is an exemplary partial fingerprint image of processed biometricdata partially captured during authentication;

FIG. 10 is a flowchart illustrating an exemplary method forauthenticating the identity of an individual using text-based biometricauthentication; and

FIG. 11 is a flowchart illustrating an alternative exemplary method forauthenticating the identity of an individual using text-based biometricauthentication.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is an expanded block diagram of an exemplary embodiment of aserver architecture of an authentication computer (AC) system 10 usedfor authenticating the identity of an individual. The AC system 10includes a server system 12 and client computer systems 14. Clientcomputer systems 14 are generally operated by any individual authorizedto access the server system 12 such as, but not limited to, employees ofentities that administer public or private programs. In the exemplaryembodiment, the server system 12 includes components such as, but notlimited to, a database server 16 and an application server 18. A diskstorage unit 20 is coupled to the database server 16. It should beappreciated that the disk storage unit 20 may be any kind of datastorage and may store any kind of data including, but not limited to, atleast one conversion algorithm, captured raw biometric data, biometrictemplate data, and identity records that include at least biographicdata and enrollment biometric words. Servers 16 and 18 are coupled in alocal area network (LAN) 22. However, it should be appreciated that inother embodiments the servers 16 and 18 may be coupled together in anymanner including in a wide area network (WAN) 24. Moreover, it should beappreciated that in other embodiments additional servers may be includedin the server system 12 that perform the same or different functions asservers 16 and 18.

The database server 16 is connected to a database that is stored on thedisk storage unit 20, and can be accessed by authorized users from anyof the client computer systems 14 in any manner that facilitatesauthenticating individuals as described herein. The database may beconfigured to store documents in any type of database including, but notlimited to, a relational object database or a hierarchical database.Moreover the database may be configured to store data in formats suchas, but not limited to, text documents and binary documents. In analternative embodiment, the database is stored remotely from the serversystem 12. The server system 12 is configured to conduct any type ofmatching of any feature or information associated with individuals asdescribed herein. The server system 12 is also configured to determineat least one conversion algorithm for converting biometric data intowords.

The server system 12 is typically configured to be communicativelycoupled to client computer systems 14 using the Local Area Network (LAN)22. However, it should be appreciated that in other embodiments, theserver system 12 may be communicatively coupled to end users at computersystems 14 via any kind of network including, but not limited to, a WideArea Network (WAN), the Internet, and any combination of LAN, WAN andthe Internet. Any authorized end user at the client computer systems 14can access the server system 12, and authorized client computer systems14 may automatically access the computer system 12 and vice versa.

In the exemplary embodiment, the client computer systems 14 may becomputer systems associated with entities that administer programsrequiring improved identity authentication. Such programs include, butare not limited to, driver licensing programs, Visa programs, nationalidentity programs, offender programs, welfare programs and taxpayerregistration programs. Moreover, each client system 14 may be used tomanage and administer a plurality of such programs. Each of the clientcomputer systems 14 includes at least one personal computer 26configured to communicate with the server system 12. Moreover, thepersonal computers 26 include devices, such as, but not limited to, aCD-ROM drive for reading data from computer-readable recording mediums,such as a compact disc-read only memory (CD-ROM), a magneto-optical disc(MOD) and a digital versatile disc (DVD). Additionally, the personalcomputers 26 include a memory (not shown). Moreover, the personalcomputers 26 include display devices, such as, but not limited to,liquid crystal displays (LCD), cathode ray tubes (CRT) and colormonitors. Furthermore, the personal computers 26 include printers andinput devices such as, but not limited to, a mouse (not shown), keypad(not shown), a keyboard, a microphone (not shown), and biometric capturedevices 28.

Although the client computer systems 14 include personal computers 26 inthe exemplary embodiment, it should be appreciated that in otherembodiments the client computer systems 14 may include portablecommunications devices capable of at least displaying messages andimages, and capturing and transmitting authentication data. Suchportable communications devices include, but are not limited to, a smartphone and any type of portable communications device having wirelesscapabilities such as a personal digital assistant (PDA) and a laptopcomputer. Moreover, it should be appreciated that in other embodimentsthe client computer systems 14 may include any computer system thatfacilitates authenticating the identity of an individual as describedherein such as, but not limited to, server systems.

Each of the biometric capture devices 28 includes hardware configured tocapture at least one specific type of biometric sample. In the exemplaryembodiment, each biometric capture device 28 may be any device thatcaptures any type of desired biometric sample. Such devices include, butare not limited to, microphones, iris scanners, fingerprint scanners,vascular scanners and digital cameras. Thus, each of the client systems14 is configured to at least capture biometric data for a desiredbiometric type from an individual. It should be appreciated thatalthough the exemplary embodiment includes two client computer systems14 each including at least one personal computer 26, in otherembodiments any number of client computer systems 14 may be provided andeach of the client computer systems 14 may include any number ofpersonal computers 26.

Application server 18 and each personal computer 26 includes a processor(not shown) and a memory (not shown). It should be understood that, asused herein, the term processor is not limited to just those integratedcircuits referred to in the art as a processor, but broadly refers to acomputer, an application specific integrated circuit, and any otherprogrammable circuit. It should be understood that computer programs, orinstructions, are stored on a computer-readable recording medium, suchas the memory (not shown) of application server 18 and of the personalcomputers 26, and are executed by the processor. The above examples areexemplary only, and are thus not intended to limit in any way thedefinition and/or meaning of the term “processor.”

The memory (not shown) included in application server 18 and in thepersonal computers 26, can be implemented using any appropriatecombination of alterable, volatile or non-volatile memory ornon-alterable, or fixed, memory. The alterable memory, whether volatileor non-volatile, can be implemented using any one or more of static ordynamic RAM (Random Access Memory), a floppy disc and disc drive, awriteable or re-writeable optical disc and disc drive, a hard drive,flash memory or the like. Similarly, the non-alterable or fixed memorycan be implemented using any one or more of ROM (Read-Only Memory), PROM(Programmable Read-Only Memory), EPROM (Erasable Programmable Read-OnlyMemory), EEPROM (Electrically Erasable Programmable Read-Only Memory),an optical ROM disc, such as a CD-ROM or DVD-ROM disc, and disc drive orthe like.

It should be appreciated that the memory of the application server 18and of the personal computers 26 is used to store executableinstructions, applications or computer programs, thereon. The terms“computer program” and “application” are intended to encompass anexecutable program that exists permanently or temporarily on anycomputer-readable recordable medium that causes the computer or computerprocessor to execute the program. In the exemplary embodiment, a parserapplication and a generic filtering module (GFM) application are storedin the memory of the application server 18. The parser applicationcauses the application server 18 to convert biometric data into at leasttext strings according to a determined conversion algorithm. At leastone of the text-strings is included in a probe that may be generated bythe GFM application. The probe may also be generated by anotherapplication, different than the GFM application, stored in the serversystem 12 or any of the client systems 14. Text strings are also knownas words. The probe may include any data such as, but not limited to,words. Specifically, words generated from biometric data captured duringenrollment are referred to herein as enrollment biometric words andwords generated from biometric data captured during authentication arereferred to herein as authentication words.

The GFM application is a text search engine which causes the applicationserver 18 to compare the probe against identity records stored in theserver system 12. Moreover, the GFM application causes the applicationserver 18 to generate a list of potential matching identity recordsaccording to the similarity between the probe and the identity recordsin the server system 12. Furthermore, the GFM application causes theapplication server 18 to determine the similarity between the probe andidentity records using one of a plurality of authentication policies andrules included in the GFM application itself. However, it should beappreciated that in other embodiments the authentication policies andrules may not be included in the GFM application. Instead, theauthentication policies and rules may be stored in the server system 12separate from the GFM application or in any of the client systems 14. Itshould be understood that the authentication policies may determine thesimilarity between a probe and the identity records on any basis, suchas, but not limited to, according to the number of matching wordsbetween the probe and each of the identity records. Although the parserapplication is stored in the application server 18 in the exemplaryembodiment, it should be appreciated that in other embodiments theparser application may be stored in any of the client systems 14.

FIG. 2 is a plan view of an exemplary fingerprint image 30 of processedbiometric data. Specifically, the fingerprint image 30 constitutesbiometric data captured from an individual using one of the biometriccapture devices 28, and includes biometric features such as, but notlimited to, ridge endings and ridge bifurcations. Because thesebiometric features constitute small discrete points in the fingerprint30, they are referred to as minutia points MPn. Thus, the minutia pointsMPn represent biometric features of the captured biometric data. Byvirtue of determining the locations of minutia points MPn within thefingerprint image 30 and including the minutia points MPn as data in abiometric feature template, the biometric features are extracted fromthe captured fingerprint biometric data and are included as data in thebiometric feature template. It should be understood that biometricfeature templates are usually a smaller compact representation of thebiometric features included in the captured biometric data, and are usedfor authenticating individuals. The captured biometric data is usuallyarchived.

Although the captured biometric data is from a fingerprint in theexemplary embodiments described herein, it should be appreciated that inother embodiments the captured biometric data may be from any otherbiometric type or combinations of biometric types including, but notlimited to, face, voice, and iris. Moreover, it should be appreciatedthat such other biometric types may have biometric features differentthan the biometric features of fingerprints that can be extracted fromthe captured biometric data and included in a biometric featuretemplate. For example, when iris biometric data is captured duringauthentication, phase information and masking information of the irismay be extracted from the captured iris biometric data and included asdata in a biometric feature template. Although the captured biometricdata is processed into a biometric feature template in the exemplaryembodiment, it should be appreciated that in other embodiments thecaptured biometric data may be processed into any form that facilitatesauthenticating the individual, such as, but not limited to, photographsand electronic data representations.

A longitudinal direction of ridges 32 in a core 34 of the fingerprint isused to determine the orientation of the fingerprint image 30.Specifically, a Cartesian coordinate system is electronicallysuperimposed on the image 30 such that an axis Y is positioned to extendthrough the core 34 in the longitudinal direction, and another axis X ispositioned to pass through the core 34 and to perpendicularly intersectthe Y-axis at the core 34. It should be appreciated that theintersection of the X and Y axes constitutes an origin of the Cartesiancoordinate system.

FIG. 3 is the plan view of the exemplary fingerprint image 30 as shownin FIG. 2, further including a plurality of circles Ci electronicallysuperimposed on the fingerprint image 30 such that the circles Ci areconcentrically positioned about the origin of the Cartesian coordinatesystem. In the exemplary embodiment, the circles Ci are positioned suchthat they are radially uniformly separated from each other by a distanceD. It should be appreciated that the distance D may be any distance thatfacilitates authenticating the identity of an individual as describedherein.

FIG. 4 is the plan view of the exemplary fingerprint image 30 as shownin FIG. 2 further including a radial grid 36 positioned thereon fordetermining exemplary words from biometric data. Specifically, aplurality of radial lines Rj are electronically superimposed andpositioned on the fingerprint image 30 such that the circles Ci and thelines Rj together define the radial grid 36 electronically superimposedon the fingerprint image 30. Each of the radial lines Rj is separated bya same angle θ. It should be appreciated that the designations “n,” “i,”and “j,” as used in conjunction with the minutia points MPn, circles Ciand radial lines Rj, respectively, are intended to indicate that anynumber “n” of minutia points, any number “i” of circles and any number“j” of radial lines may be used that facilitates authenticating theidentity of an individual as described herein. Although the biometricfeature template data of the exemplary embodiment includes minutiapoints MPn as biometric feature data, it should be appreciated that inother embodiments the biometric feature template data may includebiometric feature data appropriate for any other biometric typeincluding, but not limited to, face, voice and iris.

The radial lines Rj and circles Ci define a plurality of intersections38 and a plurality of cells 40 in the radial grid 36. Coordinates basedon the Cartesian coordinate system are computed for each intersection 38and for each minutia point MPn to determine the position of each minutiapoint MPn relative to the radial grid 36. Specifically, the coordinatesof each minutia point MPn are compared against the coordinates of theintersections 38, to determine one of the cells 40 that corresponds toand contains, each minutia point MPn. For example, by comparing thecoordinates of the minutia point MP8 against the coordinates 38, it isdetermined that one of the cells 40 defined by radial lines R3 and R4,and circles C6 and C7, contains the minutia point MP8. Because theminutia point MP8 is contained in a cell 40 defined by radial lines R3,R4 and circles C6, C7, the position of minutia point MP8 may beexpressed in a text string using radial line and circle designationsderived from the radial grid 36. Specifically, in the exemplaryembodiment, the position of the minutia point MP8 is expressed in thealphanumeric text string R3R4C6C7. Consequently, it should be understoodthat the position of each one of the minutia points MPn may be describedtextually in an alphanumeric text string derived from its correspondingcell 40. As such, it should be understood that superimposing the radialgrid 36 on the fingerprint image 30 facilitates converting the minutiapoints MPn into text strings. It should be appreciated that any numberof minutia points MPn may be positioned in any one of the cells 40 andthat desirably, each of the minutia points MPn is positioned in a singleone of the cells 40.

Each alphanumeric text string is an alphanumeric word that facilitatestextually describing biometric features included in captured biometricdata that is to be used for authentication. Moreover, because each wordis derived from the position of a corresponding cell 40, each cell 40 ofthe radial grid 36 constitutes a word that may be used to facilitatetextually describing biometric features included in captured biometricdata. Furthermore, because the radial grid 36 includes a plurality ofcells 40, the radial grid 36 defines a plurality of words that may beused to facilitate textually describing biometric features included incaptured biometric data. Additionally, because a plurality of wordsconstitutes a vocabulary, the radial grid 36 itself constitutes avehicle for defining a vocabulary of words that may be used tofacilitate textually describing biometric features included in capturedbiometric data. By using the radial grid 36 as described in theexemplary embodiment, an algorithm is executed that converts capturedbiometric data into words, included in a vocabulary of words, that maybe used as the basis for authenticating the identity of an individual.Thus, it should be understood that by virtue of executing the conversionalgorithm, words are generated that map to the vocabulary.

A biometric data sample captured for an identical biometric type fromthe same person may vary each time the biometric data sample iscaptured. Consequently, the positions of the biometric features includedin the captured biometric data samples, and minutia points correspondingto the biometric features, may also vary. It should be appreciated thatthe minutia point variances generally do not affect the positions, andrelated words, of minutia points MPn within the grid 36. However, theminutia point variances may affect the positions, and related words, ofminutia points MPn positioned proximate to or on a border betweenadjacent cells 40. It should be appreciated that by virtue of definingthe plurality of cells 40, the radial lines Rj and circles Ci alsodefine the borders between adjacent cells 40. Thus, minutia pointspositioned proximate to or on a radial line Rj or a circle Ci, may belocated in different cells 40 in different biometric data samplescaptured for the identical biometric type from the same person. Minutiapoints MPn positioned proximate to or on a line Rj or a circle Ci arereferred to herein as borderline minutia points.

Minutia point MP3 is positioned in a first cell 40-1 proximate theborder R22 between the first cell 40-1 and a second cell 40-2 includedin the radial grid 36. Thus, minutia point MP3 is a borderline minutiapoint whose position within the grid 36 may vary between differentbiometric data samples captured for the identical biometric type fromthe same person. Specifically, the location of minutia point MP3 withinthe grid 36 may vary such that in one biometric data sample the minutiapoint MP3 is located in cell 40-1 proximate the radial line R22, and inanother biometric data sample of the identical biometric type theminutia point MP3 is located in cell 40-2 proximate radial line R22.Minutia point MP1 is also a borderline minutia point and is locatedwithin a third cell 40-3 proximate the circle C9 between the third cell40-3 and a fourth cell 40-4. Thus, the position of minutia point MP1within the grid 36 may also vary between captured biometric datasamples. That is, the position of minutia point MP1 within the grid 36may vary, similar to minutia point MP3, between cells 40-3 and 40-4 indifferent biometric data samples of an identical biometric type from thesame person. Thus, it may be difficult to accurately determine a singlecell 40 location for borderline minutia points such as MP1 and MP3.

The information shown in FIG. 5 is the same information shown in FIG. 4,but shown in a different format, as described in more detail below. Assuch, geometric and mathematical relationships illustrated in FIG. 5that are identical to geometric and mathematical relationshipsillustrated in FIG. 4, are identified using the same reference numeralsused in FIG. 4.

FIG. 5 is an enlarged partial plan view of the exemplary fingerprintimage 30 and radial grid 36 as shown in FIG. 4, further including anoverlapping border region 42-1 positioned about radial line R22 andanother overlapping border region 42-2 positioned about circle C9. Theoverlapping border region 42-1 is electronically superimposed on thegrid 36 and is formed by rotating the radial line R22 clockwise andcounterclockwise about the origin of the Cartesian coordinate system byan angle θ1. In the exemplary embodiment, the angle θ1 is one degree.The overlapping border region 42-2 is electronically superimposed on thegrid 36 and is formed by radially offsetting the circle C9 towards andaway from the center of the Cartesian coordinate system by apredetermined distance. In the exemplary embodiment, the predetermineddistance may be any distance that adequately captures borderline minutiapoints as described herein.

The overlapping border regions 42-1 and 42-2 operate to effectivelyexpand the borders of adjacent cells so that the borders of adjacentcells 40 overlap. Thus, the overlapping border regions 42-1 and 42-2effectively establish an area, representing a tolerance of positions ofminutia points MPn, about the borders R22 and C9, respectively, withinwhich the position of minutia points MP1 and MP3 may vary. Thus, itshould be appreciated that minutia points located within the overlappingborder regions 42-1 and 42-2 are borderline minutia points. Moreover, itshould be appreciated that the overlapping border regions 42-1 and 42-2may be used to determine borderline minutia points. Furthermore, itshould be appreciated that by effectively establishing an area withinwhich the positions of minutia points may vary, the overlapping borderregions 42-1 and 42-2 facilitate accounting for variances that may beintroduced while capturing biometric data and thus facilitate increasingthe accuracy of text-based biometric authentication as described herein.

In the exemplary embodiment, minutia point MP3 is located within theoverlapping border region 42-1. Thus, to account for the possiblepositional variation of minutia point MP3, in the exemplary embodimentminutia point MP3 is considered to have two positions within the grid36. That is, the minutia point MP3 is considered to be positioned inadjacent cells 40-1 and 40-2, and is described using words derived fromadjacent cells 40-1 and 40-2. Specifically, the position of minutiapoint MP3 is described with the words R21R22C6C7 R22R23C6C7. Minutiapoint MP1 is located within the overlapping border region 42-2, and isalso considered to have two positions within the grid 36. That is,minutia point MP1 is considered to be positioned in adjacent cells 40-3and 40-4, and is described with words derived from cells 40-3 and 40-4.Specifically, the position of minutia point MP1 is described with thewords R22R23C8C9 R22R23C9C10. It should be understood that multiplewords may constitute a sentence. Thus, because the words describing thepositions of the minutia points MP1 and MP3 constitute multiple words,the words describing the positions of the minutia points MP1 and MP3 aresentences.

It should be understood that the borderline minutia points MP1 and MP3as described in the exemplary embodiment are positioned withinoverlapping border regions 42-2 and 42-1, respectively, and thus aredescribed with words derived from two different cells 40. However, itshould be appreciated that in other embodiments, borderline minutiapoints may be located at an intersection of different overlapping borderregions, such as at the intersection of overlapping border regions 42-1and 42-2. Such borderline minutia points located at the intersection oftwo different overlapping border regions are considered to have fourdifferent cell positions within the grid 36, and are described withwords derived from the four different cells.

Although the exemplary embodiment is described as using an angle θ1 ofone degree, it should be appreciated that in other embodiments the angleθ1 may be any angle that is considered to define an overlapping borderregion large enough to capture likely borderline minutia points.Moreover, in other embodiments, instead of rotating the radial line R22by the angle θ1 to define the overlapping border region 42-1, the radialline R22 may be offset to each side by a predetermined perpendiculardistance, adequate to capture likely borderline minutia points, todefine the overlapping border region 42-1. It should also be appreciatedthat although the exemplary embodiment is described using only oneoverlapping border region 42-1 for one radial line R22, and only oneoverlapping border region 42-2 for one circle C9, in other embodimentsoverlapping border regions may be positioned about each radial line Rjand each circle Ci, or any number of radial lines Rj and circles Ci thatfacilitates deriving words for borderline minutia points as describedherein.

In the exemplary embodiment, the words are defined such that the radiallines Rj are expressed first in sequentially increasing order, followedby the circles Ci which are also expressed in sequentially increasingorder. It should be appreciated that in other embodiments the radiallines Rj and the circles Ci may be expressed in any order. Moreover, itshould be appreciated that although the exemplary embodiment expressesthe location of minutia points MPn in alphanumeric words, in otherembodiments the words may be expressed in any manner, such as, but notlimited to, only alphabetic characters and only numeric characters, thatfacilitates authenticating the identity of an individual as describedherein.

The information shown in FIG. 6 is the same information shown in FIG. 4,but shown in a different format, as described in more detail below. Assuch, geometric and mathematical relationships illustrated in FIG. 6that are identical to geometric and mathematical relationshipsillustrated in FIG. 4, are identified using the same reference numeralsused in FIG. 4.

FIG. 6 is the plan view of the exemplary fingerprint image 30 and radialgrid 36 as shown in FIG. 4, and is for determining alternative exemplarywords from captured biometric data. In this alternative exemplaryembodiment, each adjacent pair of the radial lines Rj defines a sectorSk, and each adjacent pair of circles Ci defines a concentric band Bp.It should be appreciated that the designations “k” and “p” as used inconjunction with the sectors Sk and concentric bands Bp, respectively,are intended to convey that any number “k” of sectors Sk and any number“p” of concentric bands Bp may be used that facilitates authenticatingthe identity of an individual as described herein.

Coordinates based on the superimposed Cartesian coordinate system arecomputed for each intersection 38 and for each minutia point MPn todetermine the position of each minutia point MPn relative to the radialgrid 36. However, in contrast to the exemplary embodiment described withreference to FIG. 4, in this alternative exemplary embodiment, thecoordinates of each minutia point MPn are compared against thecoordinates of the intersections 38 to determine a corresponding sectorSk and a corresponding intersecting concentric band Bp that contain eachminutia point MPn. It should be appreciated that each sector Sk andconcentric band Bp designation describes a cell 40. For example, bycomparing the coordinates of the minutia point MP8 against thecoordinates 38, it is determined that the sector S3 and the concentricband B7 intersecting with sector S3, contain the minutia point MP8. Byvirtue of being contained in sector S3 and concentric band B7, theposition of minutia point MP8 may be expressed in an alphanumeric wordusing sector Sk and concentric band Bp designations derived from theradial grid 36. Specifically, the position of the minutia point MP8 maybe expressed with the word S3B7. Consequently, the position of each oneof the minutia points MPn may be described in words derived from acorresponding sector Sk and concentric band Bp. As such, it should beunderstood that superimposing the radial grid 36 on the biometric image30 facilitates converting the minutia points MPn into a vocabulary ofalphanumeric words different from the vocabulary of the exemplaryembodiment.

By using the radial grid 36 as described in this alternative exemplaryembodiment, an algorithm is executed that converts captured biometricdata into words, included in the different vocabulary of words, whichmay be used as the basis for authenticating the identity of anindividual. Thus, by virtue of executing the algorithm of thealternative exemplary embodiment, words are generated that map to thedifferent vocabulary.

In this alternative exemplary embodiment borderline minutia points suchas MP1 and MP3 are also considered to have two positions within the grid36. Thus, in this alternative exemplary embodiment, borderline minutiapoint MP1 is described with the words S22B9 S22B10 and borderlineminutia point MP3 is described with the words S21B7 S22B7.

In this alternative exemplary embodiment, the words are defined suchthat the sectors Sk are expressed first and the concentric bands Bp areexpressed second. However, it should be appreciated that in otherembodiments the sectors Sk and the concentric bands Bp may be expressedin any order that facilitates authenticating the identity of anindividual as described herein.

It should be appreciated that in yet other exemplary embodiments afterobtaining the word for each cell 40, the words may be simplified, ortranslated, to correspond to a single cell number. For example, the wordS0B0 may be translated to correspond to cell number zero; S1B0 may betranslated to correspond to cell number one; S2B0 may be translated tocorrespond to cell number two; S31B0 may be translated to correspond tocell number 31; and, S0B1 may be translated to correspond to cell number32. Thus, the words S0B0, S1B0, S2B0, S31B0 and S0B1 may be representedsimply as single cell numbers 0, 1, 2, 31 and 32, respectively.

In this alternative exemplary embodiment the words describing thepositions of minutia points MP1 and MP3 are sentences. Additionally, itshould be appreciated that when the fingerprint image 30 includes aplurality of minutia points MPn, words corresponding to the minutiapoints may be sequentially positioned adjacent each other to formsentences. Such sentences may be generated, for example, by combiningwords that are nearest to the origin of the Cartesian co-ordinatesystem, starting with word S0B0, and proceeding clockwise and outwardsto end at the word SkBp. However, in other embodiments the words are notrequired to be positioned sequentially, and may be positioned in anyorder to form a sentence that facilitates authenticating the identity ofan individual as described herein.

Although this alternative exemplary embodiment includes the same radialgrid 36 superimposed on the same biometric image 30 as the exemplaryembodiment, it should be appreciated that the same radial grid 36 may beused to generate many different vocabularies in addition to thosedescribed herein. Moreover, although both of the exemplary embodimentsdescribed herein use the same radial grid 36 to generate differentvocabularies, it should be appreciated that in other embodiments anyother medium that establishes a positional relationship with biometricfeatures of a desired biometric type may be used as a conversionalgorithm for generating at least one vocabulary of words that describesthe positions of the biometric features. Such mediums include, but arenot limited to, rectangular grids, triangular grids, electronic modelsand mathematical functions. Furthermore, it should be appreciated thatdifferent vocabularies generated from different mediums may be combinedto yield combined, or fused, vocabularies for the same biometric typeand for different biometric types.

In the exemplary embodiments described herein the grid 36 is used togenerate words that map to a corresponding vocabulary. Moreover, thegrid 36 may be used to generate many words that each map to a same ordifferent vocabulary. Furthermore, it should be understood that anyother medium that establishes a positional relationship with biometricfeatures may be used for generating words that each map to the same ordifferent vocabulary.

Using the grid 36 to generate a vocabulary of words as described in theexemplary embodiments, effectively executes an algorithm that generatesa vocabulary of words for use in authenticating the identity ofindividuals based on captured biometric data. However, it should beappreciated that in other embodiments other known algorithms, orclassification algorithms, may be used to convert biometric featuresinto words and thus generate additional alternative vocabularies. Suchother known algorithms may convert biometric features into words byanalyzing captured biometric data and classifying the captured biometricdata into one or more finite number of groups. Such known classificationalgorithms include, but are not limited to, a Henry classificationalgorithm. The Henry classification algorithm examines a fingerprintglobal ridge pattern and classifies the fingerprint based on the globalridge pattern into one of a small number of possible groups, orpatterns.

Consequently, in yet another alternative exemplary embodiment, anothervocabulary of alphanumeric words may be generated by mapping each Henryclassification pattern to a corresponding word included in a vocabularydefined for the Henry classification algorithm. For example, an archpattern in the Henry classification algorithm may be mapped, orassigned, the corresponding word “P1,” and a left loop pattern may bemapped, or assigned, the corresponding word “P2.” It should beappreciated that in other embodiments, vocabularies of words andsentences may be established for any classification algorithm, thusfacilitating use of substantially all known classification algorithms toauthenticate the identity of individuals as described herein. It shouldbe appreciated that other classification algorithms may rely ondistances between groups or bins. In such classification algorithms, alexicographic text-encoding scheme for numeric data that preservesnumeric comparison operators may be used. Such numerical comparisonoperators include, but are not limited to, a greater than symbol (>),and a less than symbol (<). Further examples of fingerprintclassification techniques that could be utilized using this approachinclude, but are not limited to, ridge flow classification, ridge flowin a given fingerprint region, ridge counts between minutiae points,lines between minutiae points, and polygons formed between minutiaepoints.

As discussed above, using the grid 36 as described in the exemplaryembodiments effectively constitutes executing an algorithm thatgenerates a vocabulary of words that can be independently used forbiometrically authenticating individuals, and that generates many wordsthat each map to a same or different vocabulary. It should also beappreciated that other algorithms may be used to convert biometricfeatures into words to generate vocabularies of words for differentbiometric features of the same biometric type that may be independentlyused for authentication. Such other algorithms may also generate wordsthat each map to the same or different vocabulary.

In yet another alternative embodiment, another algorithm may generate anadditional vocabulary of words and sentences derived from the overallridge pattern of a fingerprint instead of from fingerprint ridge endingsand ridge bifurcations. Combining, or fusing, vocabularies that includewords for the same biometric type, but for different biometric features,provides a larger amount of information that can be used to generatemore trustworthy authentication results. Thus, it should be appreciatedthat by combining or fusing vocabularies, additional new vocabulariesrepresenting a same biometric type and different biometric features maybe generated such that different words, from the combined vocabulary,representing the same biometric type may be used to generate moretrustworthy authentication results. For example, when authenticating theidentity of an individual on the basis of fingerprint biometric data,the identity may be authenticated using appropriate words from avocabulary derived from fingerprint ridge endings and ridgebifurcations, and words from another vocabulary derived from the overallridge pattern of the fingerprint. It should be appreciated thatauthenticating the identity of an individual using different words froma combined vocabulary representing the same biometric type and differentbiometric features facilitates increasing the level of trust in theauthentication results. It should be understood that by virtue ofgenerating a vocabulary of words each algorithm also defines thevocabulary of words. Moreover, it should be appreciated that eachdifferent algorithm generates and defines a different vocabulary ofwords.

The exemplary embodiments described herein use algorithms to convertbiometric features of fingerprints into words. Such words are includedin the vocabularies of words generated by respective algorithms.However, it should be appreciated that in other embodiments differentalgorithms may be used to convert biometric features, of any desiredbiometric type, into words. These words are also included in thevocabularies of words generated by the respective different algorithms.For example, a first algorithm may convert biometric features of theiris into words included in a first vocabulary of words generated by thefirst algorithm, and a second algorithm, different than the firstalgorithm, may convert biometric features of the voice into wordsincluded in a second vocabulary of words generated by the secondalgorithm. It should be understood that an additional third vocabularyof words including the first and second vocabularies may be generated bycombining, or fusing, the first and second vocabularies. Combining, orfusing, vocabularies that define words for different biometric typesalso provides a larger amount of information that can be used togenerate more trustworthy authentication results. Thus, it should beappreciated that by combining or fusing vocabularies, additional newvocabularies representing different biometric types may be generatedsuch that different words, from the combined vocabulary, representingdifferent biometric types may be used to generate more trustworthyauthentication results. For example, when authenticating the identity ofan individual on the basis of iris and voice biometric data, theidentity may be authenticated using appropriate words from the firstvocabulary and appropriate words from the second vocabulary. It shouldbe appreciated that authenticating the identity of an individual usingdifferent words from a fused vocabulary representing different biometrictypes facilitates increasing the level of trust in the authenticationresults.

When a plurality of biometric types are used for authentication,configurable authentication policies and rules included in the GFMapplication may be configured to weight some biometric types differentlythan others. Authentication based on certain biometric types is moretrustworthy than authentication based on other biometric types. Forexample, a biometric authentication result based on biometric datacaptured from an iris may often be more trustworthy than anauthentication result based on biometric data captured from afingerprint. In order to account for the different levels of trust inthe authentication results, each biometric type may be weighteddifferently. For example, in a fused vocabulary certain words may bedirected towards a fingerprint of an individual and other words may bedirected towards an iris of the same individual. Because authenticationbased on an iris may be considered more trustworthy, duringauthentication the iris words are given greater emphasis, or are moreheavily weighted, than the fingerprint words. It should be appreciatedthat weighting biometric data of one biometric type differently thanbiometric data of another biometric type by emphasizing the biometricdata of the one biometric type more than the biometric data of the otherbiometric type may yield more trustworthy authentication results.

Words in fused vocabularies may also be weighted due to the source ofthe original words before fusion. For example, words from the vocabularygenerated using the method of the exemplary embodiment may be weightedmore heavily than words from the vocabulary generated using thealternative exemplary embodiment. Different types of words generatedfrom the same biometric type may also be weighted differently. Forexample, elderly individuals may be associated with certain types ofwords that identify them as elderly. Weighting such certain types ofwords more heavily during biometric authentication may facilitatereducing the time required for authentication by reducing the number ofcomparisons against those identity records having the same certain typesof words.

It should be understood that converting captured biometric data intowords, as described herein, facilitates enabling the server system 12 toimplement matching algorithms using industry standard search engines.Moreover, it should be understood that performing industry standardsearches based on such words facilitates enabling the server system 12to generate and return results to the client systems 14 more efficientlyand more cost effectively than existing biometric systems and methods,and facilitates reducing dependence on expensive, specialized, andproprietary biometric matchers used in existing biometric authenticationsystems and methods.

FIG. 7 is an exemplary identity record 44 including biographic data 46collected from an individual, the type 48 of biometric data obtainedfrom the individual, and words 50 for each biometric type 48. In orderto authenticate the identity of individuals with the server system 12,the biographic data 46 and biometric data of a plurality of individualsshould be collected and stored in the server system 12 prior toauthentication. The words 50 should also be determined and stored in thesystem 12 prior to authentication. Obtaining and storing such data priorto authentication is generally known as enrollment. In the exemplaryembodiment at least the biographic data 46 and words 50 for eachindividual enrolled in the server system 12 are included in acorresponding identity record stored in the server system 12. Theidentity records 44 may also include data such as, but not limited to,the obtained biometric data and biometric feature templates. Moreover,it should be appreciated that the identity records 44 stored in theserver system 12 constitute a gallery of identity record data.

In the exemplary embodiment, during enrollment each individual manuallytypes the desired biographic data 46 into the keyboard associated withone of the client systems 14. In order to properly capture desiredbiometric data, the client systems 14 are configured to includeenrollment screens appropriate for capturing the desired biometric data,and are configured to include the biometric capture devices 28 forcapturing the desired biometric data submitted by the individuals.However, in other embodiments, the biographic data 46 and biometric datamay be obtained using any method that facilitates enrolling individualsin the system 12. Such methods include, but are not limited to,automatically reading the desired biographic data 46 and biometric datafrom identity documents and extracting the desired biographic data 46and biometric data from other databases positioned at differentlocations than the client system 14. Such identity documents include,but are not limited to, passports and driver's licenses. It should beunderstood that enrollment data of individuals constitutes at least thebiographic data 46 and the words 50 derived from the desired biometricdata.

The term “biographic data” 46 as used herein includes any demographicinformation regarding an individual as well as contact informationpertinent to the individual. Such demographic information includes, butis not limited to, an individual's name, age, date of birth, address,citizenship and marital status. Moreover, biographic data 46 may includecontact information such as, but not limited to, telephone numbers ande-mail addresses. However, it should be appreciated that in otherembodiments any desired biographic data 46 may be required, or,alternatively, in other embodiments biographic data 46 may not berequired.

After obtaining the desired biometric data during enrollment, thedesired biometric data is converted into words 50 with a conversionalgorithm. In the exemplary embodiment, the desired biometric data isthe left index finger. Thus, during enrollment biometric data of theleft index finger is captured and is converted into a corresponding textstring 50, or words 50, using the algorithm of the exemplary embodimentas described with respect to FIG. 4. It should be understood that eachtext string 50 constitutes a word 50 that facilitates textuallydescribing biometric features included in captured biometric data.Because the words 50 are generated from biometric data captured duringenrollment, the words 50 may also be referred to as enrollment biometricwords 50. Thus, each of the identity records 44 includes enrollmentbiometric words 50 of an individual determined during enrollment.

It should be appreciated that the words R22R23C8C9 R22R23C9C10 andR21R22C6C7 R22R23C6C7 describe minutia points MP1 and MP3, respectively.Moreover, it should be appreciated that in other embodiments, words 50describing minutia points of the left index finger may include a prefix,such as, but not limited to, FLI which abbreviates Finger—Left Index.Likewise, words 50 describing minutia points of the right index fingermay include a prefix such as, but not limited to, FRI which abbreviatesFinger—Right Index. Thus, the word 50 describing exemplary minutia pointMP1 may be represented as FLIR22R23C8C9 FLIR22R23C9C10.

Although the words 50 are described in the exemplary embodiment as beinggenerated from biometric data captured during enrollment, in otherembodiments additional words 50, derived from biometric data obtainedafter enrollment, may be added to an identity record 44 afterenrollment. Moreover, in other embodiments the words 50 may includewords 50 generated from different types 48 of biometric data such as,but not limited to, face, iris and voice biometric data. Words 50,corresponding to the different types of biometric data, are generallygenerated by different algorithms. Words 50 generated by differentalgorithms for a same biometric type may also be included in theidentity records 44.

Although the identity records 44 are stored as record data in the serversystem 12 in the exemplary embodiment, it should be appreciated that inother embodiments the identity records 44 may be stored in any form suchas, but not limited to, text documents, XML documents and binary data.

The information shown in FIG. 8 is substantially the same informationshown in FIG. 7, but includes words 50 that were converted using theradial grid 36 as described herein in the alternative exemplaryembodiment associated with FIG. 6. As such, information illustrated inFIG. 8 that is identical to information illustrated in FIG. 7, isidentified using the same reference numerals used in FIG. 7.

FIG. 8 is an alternative exemplary identity record 44 includingbiographic data 46, types of biometric data 48 and words 50.

The information shown in FIG. 9 is similar to the information shown inFIG. 2, but includes a partial left index fingerprint image instead of afull left index fingerprint image, as described in more detail below. Assuch, the information illustrated in FIG. 9 that is identical toinformation illustrated in FIG. 2, is identified using the samereference numerals used in FIG. 2.

FIG. 9 is an exemplary partial fingerprint image 52 of processedbiometric data partially captured during authentication. Specifically,the partial fingerprint image 54 is of a left index fingerprint capturedfrom an individual during authentication in the exemplary embodiment. Itshould be understood that the partial fingerprint image 52 and thefingerprint image 30 are from the same finger of the same person.However, the partial fingerprint image 52 does not contain the samenumber of minutia points MPn as the fingerprint image 30. Moreover, itshould be understood that such a partial print is generally used as thebasis for authenticating the identity of an individual duringauthentication. Although the partial fingerprint image 52 is of a leftindex fingerprint, it should be appreciated that in other embodimentsfingerprints of varying quality may be obtained from the same person.Such fingerprints include, but are not limited to, rotated fingerprints.It should be appreciated that in the exemplary embodiment, allfingerprints are to be rotated to have an orientation reconciled withthat of a corresponding record fingerprint prior to properauthentication.

FIG. 10 is a flowchart 54 illustrating an exemplary method forauthenticating the identity of an individual using text-based biometricauthentication. The method starts 56 by capturing biometric data 58corresponding to a desired biometric type from the individual, andprocessing the captured biometric data into a biometric featuretemplate. In the exemplary method, the desired biometric type is theleft index finger. Thus, the data included in the biometric featuretemplate constitutes minutia points MPn of the left index finger.However, in other embodiments biometric data of any biometric type, orany combination of the same or different biometric types, may becaptured and processed into a plurality of corresponding biometricfeature templates. Such biometric types include, but are not limited to,face, finger, iris and voice. Thus, it should be understood that thecaptured biometric data may be processed into at least one biometricfeature template and that the at least one biometric feature templateincludes at least one feature.

The method continues by determining 60 one of a plurality of algorithmsfor converting biometric features of the desired biometric type intowords. The server system 12 determines the one conversion algorithm inaccordance with authentication policies stored therein. In the exemplarymethod the same conversion algorithm is used for converting biometricfeature template data into words as was used during enrollment. Althoughthe one conversion algorithm is determined using authentication policiesin the exemplary embodiment, it should be understood that in otherembodiments the server system 12 may not have authentication policiesstored therein. In such other embodiments a single conversion algorithmis stored in the server system and is determined to be the algorithmused for converting biometric features into words.

Next, the method continues by converting 62 the data included in thebiometric feature template into at least one word using the determinedconversion algorithm and including the at least one word in a probegenerated by the system 12. Words generated as a result of convertingthe biometric feature template data during authentication areauthentication words. Although biometric data of one biometric type iscaptured in the exemplary embodiment, it should be appreciated that inother embodiments biometric data may be captured for a plurality ofdifferent biometric types. In such other embodiments the capturedbiometric data for each biometric type is processed into a respectivebiometric feature template, and a conversion algorithm is determined foreach of the different biometric types such that the data included ineach of the respective biometric feature templates may be converted intoat least an authentication word. The authentication words are includedin the probe.

After including the authentication words in the probe 62, the methodcontinues by filtering 64 with the generic filtering module (GFM)application by comparing the probe against the gallery. Specifically,the GFM application compares 64 the authentication words included in theprobe against the enrollment biometric words 50 included in each of theidentity records 44 to determine potential matching identity records. Itshould be appreciated that a list of potential matching identity recordsis generated by the GFM application according to the similarity betweenthe probe and the identity records 44.

In the exemplary embodiment, when a comparison does not result in amatch between at least one authentication word in the probe and at leastone enrollment biometric word 50 in a given identity record 44, thegiven identity record 44 is discarded, or filtered out. Moreover, when acomparison does not result in a match between at least oneauthentication word in the probe and at least one enrollment biometricword 50 in any of the identity records 44, the method continues bycommunicating 66 a negative result to the client system 14. The clientsystem 14 then displays a message indicating “No Matches,” and themethod ends 68. Although the client system 14 displays a messageindicating “No Matches” when a comparison does not result in a match inthe exemplary embodiment, it should be appreciated that in otherembodiments the client system may communicate the negative result in analternative message or in any manner, including, but not limited to,emitting a sound and sending a communication to another system orprocess.

However, when at least one authentication word included in the probematches at least one enrollment biometric word included in at least oneidentity record 44, processing continues by identifying the at least oneidentity record 44 containing the at least one matching enrollmentbiometric word as a potential matching identity record. After comparing68 the probe against all of the identity records 44 in the gallery,processing continues by generating the list of potential matchingidentity records from the potential matching records. The list ofpotential matching identity records includes a listing of identityrecord identifiers that each correspond to a different one of thepotential matching identity records. In other embodiments the list mayinclude any data that facilitates identifying the potential matchingidentity records.

Next, processing continues by ranking 70 the potential matching identityrecords included in the list in accordance with the authenticationpolicies and rules included in the server system 12. For example, theauthentication policies and rules may rank the potential matchingidentity records according to the number of enrollment biometric wordscontained therein that match against authentication words in the probe.Thus, the greater the number of matching enrollment biometric wordscontained in a potential matching identity record, the more similar apotential matching identity record is to the probe. Consequently, themore similar a potential matching identity record is to the probe, thehigher the ranking of the potential matching identity record in thelist. It should be understood that the most highly ranked potentialmatching identity records in the list are most likely to be truematching identity records that may be used to authenticate the identityof the individual. After ranking the potential matching identity records70 in the list, the list of ranked potential matching identity recordsis stored in the server system 12. Processing continues by communicating72 the list of ranked potential matching identity records and the rankedmatching identity records themselves to a client system 14 for anydesired use by an entity associated with the client system 14. Forexample, the entity may use the ranked potential matching identityrecords to authenticate the individual. Next, processing ends 68.

Although the exemplary method determines a potential matching identityrecord when at least one authentication word in a probe matches at leastone enrollment biometric word in an identity record 44, it should beappreciated that in other embodiments any other matching criteria may beestablished to determine a potential matching identity record thatfacilitates authenticating the identity of an individual as describedherein. Such other criteria include, but are not limited to, determininga potential matching identity record when two or more words matchbetween a probe and an identity record 44. Although the GFM applicationranks the potential matching identity records according to the number ofmatching words contained therein in the exemplary method, it should beappreciated that in other embodiments the GFM application may rank thepotential matching identity records in accordance with any policy, ormay rank the potential matching identity records in any manner, thatfacilitates ranking the potential matching identity records based onsimilarity with the probe.

The information shown in FIG. 11 is the same information shown in FIG.10 as described in more detail below. As such, operations illustrated inFIG. 11 that are identical to operations illustrated in FIG. 10, areidentified using the same reference numerals used in FIG. 10.

FIG. 11 is a flowchart 74 illustrating an alternative exemplary methodfor authenticating the identity of an individual using text-basedbiometric authentication. This alternative embodiment is similar to thatshown in FIG. 10. However, instead of communicating the list andpotential matching identity records to an entity after ranking thepotential matching identity records 70, the server system 12 continuesprocessing by verifying the identity 76 of the individual by conductinga 1:1 verification matching transaction. More specifically, the serversystem 12 performs a subsequent process by conducting a 1:1 verificationmatching transaction between the biometric feature template andcorresponding biometric feature templates included in each of the rankedpotential matching identity records. Thus, the server system 12generates highly trusted authentication results. It should beappreciated that in other embodiments any biographic data 46, any words50, or any combination of biographic data 46 and words 50, included ineach of the ranked potential matching identity records may be used toverify the identity 76 of the individual. When the biometric featuretemplate matches the corresponding biometric feature template of atleast one of the ranked potential matching identity records, theidentity of the individual is verified 76, and a positive result iscommunicated 78 to the client system 14 and displayed for use by theentity associated with the client system 14. Specifically, the positiveresult is a message that indicates “Identity Confirmed.” Next,processing ends 68.

However, when the identity of the individual is not verified 76, anegative result is output 80 to the client system 14. Specifically, theclient system 14 displays the negative result as a message thatindicates “Identity Not Confirmed.” Next, processing ends 68.

It should be appreciated that comparing the authentication wordsincluded in a probe against the enrollment biometric words included inthe identity records constitutes an initial filtering process becausethe number of identity records to be analyzed in a subsequent 1:1verification transaction is quickly reduced to a list of potentialmatching identity records. By thus quickly reducing the number ofidentity records, the initial filtering process facilitates reducing thetime required to biometrically authenticate individuals. Thus, it shouldbe understood that by filtering out non-matching identity records toquickly generate the list of potential matching identity records, and bygenerating highly trusted authentication results 76 from the list ofpotential matching identity records, a method of text-based biometricauthentication is provided that facilitates accurately, quickly, andcost effectively authenticating the identity of individuals.

Although the probe includes authentication words in the exemplarymethods described herein, it should be appreciated that in other methodsthe probe may include a combination of biographic words andauthentication words. In such other methods, the biographic wordsconstitute words representing any biographic data such as, but notlimited to, words describing an individual's name, words describing anindividual's date of birth, and alphanumeric words describing anindividual's address. The biographic data 46 may also be included in theidentity records 44 as biographic words.

It should be understood that by virtue of including the combination ofbiographic words and authentication words in the probe, the wholeidentity of an individual may be used for authentication. Moreover, itshould be understood that using the whole identity of an individual forauthentication facilitates increasing confidence in authenticationresults. Authentication based on the whole identity of an individual asdescribed herein is unified identity searching. Thus, including thecombination of biographic words and authentication words in the probefacilitates enabling unified identity searching and facilitatesenhancing increased confidence in authentication results. It should beappreciated that in unified identity searching, identity records aredetermined to be potential matching identity records when at least oneof the biographic words included in the probe, or at least one of theauthentication words included in the probe, matches at least one of thebiographic words or one of the enrollment biometric words, respectively,included in an identity record. Furthermore, when unified identitymatching is implemented, a list of potential matching identity recordsis generated and processed as described herein in the exemplary methodwith regard to the flowchart 54.

In the exemplary embodiments described herein, biometric authenticationbased on words is used to facilitate authenticating the identities ofindividuals. More specifically, a determined algorithm convertsbiometric feature template data into authentication words. Theauthentication words are used in an initial filtering process togenerate a list of ranked potential matching identity records. The listof ranked potential matching identity records and the identity recordsthemselves are communicated to an entity for any use desired by theentity. Instead of communicating the list to an entity, a subsequentprocess may be conducted by performing a 1:1 verification matchingtransaction between the biometric feature template data included in aprobe against each of the ranked potential matching identity records toauthentication the individual. Because the text-based searching of theinitial filtering process is more efficient, less time consuming andless expensive than image based searching, the identity of an individualis facilitated to be authenticated quickly, accurately and costeffectively. Moreover, it should be appreciated that conductingtext-based searching as described herein, facilitates leveragingindustry standard search engines to facilitate increasing the efficiencyof biometric authentication, to facilitate reducing the time and costsassociated with such authentications, and to facilitate easiermodification of known biometric authentication search engines such thatknown search engines may operate with other authentication systems.Furthermore, text-based searching as described herein facilitatesenhancing continued investment in search engine technology.

Exemplary embodiments of methods for authenticating the identity of anindividual using biometric text-based authentication techniques aredescribed above in detail. The methods are not limited to use asdescribed herein, but rather, the methods may be utilized independentlyand separately from other methods described herein. Moreover, theinvention is not limited to the embodiments of the method describedabove in detail. Rather, other variations of the method may be utilizedwithin the spirit and scope of the claims.

Furthermore, the present invention can be implemented as a programstored on a computer-readable recording medium, that causes a computerto execute the methods described herein to authenticate the identity ofan individual using words derived from biometric feature templates. Theprogram can be distributed via a computer-readable storage medium suchas, but not limited to, a CD-ROM.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

What is claimed is:
 1. A method of text-based biometric authenticationcomprising: capturing biometric data for a desired biometric type froman individual using a client system; communicating the capturedbiometric data to a server system, the server system having identityrecords stored therein, each identity record including enrollmentbiometric words that represent a plurality of different biometric types;determining an algorithm for converting the biometric data intoauthentication words; converting the captured biometric data intoauthentication words using the algorithm; combining the authenticationwords into a sentence; comparing the sentence against each enrollmentbiometric word in each identity record; identifying at least one of theidentity records as a potential matching identity record when at leastpart of the sentence matches at least one of the enrollment biometricwords included in the at least one identity record; and generating alist of potential matching identity records.
 2. A method of text-basedbiometric authentication in accordance with claim 1 further comprising:verifying the identity of the individual by conducting a 1:1verification matching transaction between a captured biometric featuretemplate generated from the captured biometric data and a correspondingbiometric feature template included in each of the potential matchingidentity records; and communicating a positive result when the capturedbiometric feature template matches the corresponding biometric featuretemplate of at least one of the potential matching identity records. 3.A method of text-based biometric authentication in accordance with claim1, further comprising ranking the potential matching identity records inthe list of potential matching identity records in accordance withauthentication policies stored in the server system.
 4. A method oftext-based biometric authentication in accordance with claim 1, furthercomprising: mapping the authentication words and the enrollmentbiometric words generated from the desired biometric type to a firstvocabulary; and mapping the enrollment biometric words generated frombiometric types different than the desired biometric type to a fusedvocabulary, the fused vocabulary being different than the firstvocabulary.
 5. A method of text-based biometric authentication inaccordance with claim 1, further comprising adding additionalauthentication words to the identity record after enrolling theindividual in the server system, the additional authentication wordsbeing combinable with the sentence.
 6. A method of text-based biometricauthentication in accordance with claim 1, said converting operationcomprising: processing the captured biometric data into at least onebiometric feature template such that the at least one biometric featuretemplate includes a plurality of features; and converting each of thefeatures into an authentication word.
 7. A method of text-basedbiometric authentication in accordance with claim 1, further comprisingenrolling a plurality of individuals in the server system, saidenrolling operation comprising: collecting biographic data and capturingbiometric data from a plurality of individuals, the captured biometricdata corresponding to different biometric types; converting thebiometric data of each of the individuals into enrollment biometricwords; and storing the collected biographic data and the enrollmentbiometric words of each individual in a corresponding one of theidentity records in the server system.
 8. A method of text-basedbiometric authentication in accordance with claim 1, further comprising:combining biographic words associated with the individual with thesentence; and comparing the combination against each enrollment word andeach biographic word in each identity record to provide unified identitysearching.
 9. A system for text-based biometric authenticationcomprising: a computer configured as a server, said server including atleast a data base, said server being configured to store within saiddatabase at least one conversion algorithm and at least a gallery ofdata comprising identity records, each identity record including atleast biographic data of an individual and enrollment biometric words ofthe individual, the enrollment biometric words being generated from adesired biometric type and from biometric types different than thedesired biometric type; and at least one client system comprising atleast a computer configured to communicate with said server, said clientsystem being configured to at least capture biometric data for thedesired biometric type from an individual, said server being furtherconfigured to convert the captured biometric data into authenticationwords by executing at least one conversion algorithm, combine theauthentication words into a sentence, compare the sentence against eachenrollment biometric word in each identity record, identify at least oneof the identity records as a potential matching identity record when atleast a part of the sentence matches at least one of the enrollmentbiometric words included in the at least one identity record, andgenerate a list of potential matching identity records.
 10. A system fortext-based biometric authentication in accordance with claim 9, saidserver being further configured to: map the authentication words and theenrollment biometric words generated from the desired biometric type toa first vocabulary; and map the enrollment biometric words generatedfrom the biometric types different than the desired biometric type to afused vocabulary, the fused vocabulary including a plurality ofvocabularies.
 11. A system for text-based biometric authentication inaccordance with claim 9, said server being further configured to addadditional authentication words to the identity record after enrollingthe individual in the server system, the additional authentication wordsmapping to at least one vocabulary.
 12. A system for biometricauthentication in accordance with claim 9, said server being furtherconfigured to verify the identity of the individual by conducting a 1:1verification matching transaction between a captured biometric featuretemplate generated from the captured biometric data and a correspondingbiometric feature template included in each of the potential matchingidentity records.
 13. A system for biometric authentication inaccordance with claim 12, said at least one client system being furtherconfigured to display an output communicating a positive result when thecaptured biometric feature template matches the corresponding biometricfeature template of at least one of the potential matching identityrecords.
 14. A system for biometric authentication in accordance withclaim 9, said server being further configured to: process the capturedbiometric data into a biometric feature template; and convert dataincluded in the biometric feature template into at least anauthentication word.
 15. A system for biometric authentication inaccordance with claim 9, each authentication word being comprised of analphanumeric text string that textually describes a biometric feature ofthe captured biometric data.
 16. A system for biometric authenticationin accordance with claim 9, wherein said server is further configuredto: convert biometric data of each of the individuals obtained duringenrollment into enrollment biometric words with the at least oneconversion algorithm; and store the collected biographic data and theenrollment biometric words of each individual in a correspondingidentity record in said data base.
 17. A computer program recorded on anon-transitory computer-readable recording medium included in anauthentication computer system for enabling authentication ofindividuals, the computer program being comprised of instructions, whichwhen read and executed by the authentication computer system, cause theauthentication computer system to perform at least the followingoperations: determine a plurality of algorithms, each of the algorithmsbeing operable to convert biometric data of a corresponding biometrictype captured from a user into a vocabulary of words; convert thecaptured biometric data for each biometric type into authenticationwords in accordance with the corresponding one of the algorithms;combine the authentication words into a sentence; compare the sentenceagainst identity records, wherein each of the identity records includesenrollment biometric words of a corresponding individual and theenrollment biometric words and the authentication words represent aplurality of different biometric types; identify at least one of theidentity records as a potential matching identity record when at leastpart of the sentence matches at least one of the enrollment biometricwords included in the at least one identity record; and generating alist of potential matching identity records.
 18. A computer programrecorded on a non-transitory computer-readable recording medium inaccordance with claim 17, further comprising instructions, which whenread and executed by the authentication computer system, cause theauthentication computer system to: verify the identity of the individualby conducting a 1:1 verification matching transaction between a capturedbiometric feature template generated from biometric data captured fromthe individual and a corresponding biometric feature template includedin each of the potential matching identity records; and communicate apositive result when the captured biometric feature template matches thecorresponding biometric feature template of at least one of thepotential matching identity records.